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Updated: Apr 5, 2026
A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors
Published on: April 29, 2022
Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic RAS and
Otto Kauko1, Teemu Daniel Laajala2, Mikael Jumppanen3
11] Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistokatu 6, FI-20520 Turku, Finland [2] Department of Pathology, University of Turku, FI-20520 Turku, Finland [3] Turku Doctoral Program of Biomedical Sciences (TuBS), Turku, Finland.
Abstract:
Hyperactivated RAS drives progression of many human malignancies. However, oncogenic activity of RAS is dependent on simultaneous inactivation of protein phosphatase 2A (PP2A) activity. Although PP2A is known to regulate some of the RAS effector pathways, it has not been systematically assessed how these proteins functionally interact. Here we have analyzed phosphoproteomes regulated by either RAS or PP2A, by phosphopeptide enrichment followed by mass-spectrometry-based label-free quantification. To allow data normalization in situations where depletion of RAS or PP2A inhibitor CIP2A causes a large uni-directional change in the phosphopeptide abundance, we developed a novel normalization strategy, named pairwise normalization. This normalization is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of the pairwise normalization was verified by various independent methods. Additionally, we demonstrate how the selected normalization method influences the downstream analyses and interpretation of pathway activities. Consequently, bioinformatics analysis of RAS and CIP2A regulated phosphoproteomes revealed a significant overlap in their functional pathways. This is most likely biologically meaningful as we observed a synergistic survival effect between CIP2A and RAS expression as well as KRAS activating mutations in TCGA pan-cancer data set, and synergistic relationship between CIP2A and KRAS depletion in colony growth assays.
Insights
Hyperactivated RAS drives cancer, but requires protein phosphatase 2A (PP2A) inactivation. This study reveals overlapping pathways regulated by RAS and CIP2A, suggesting a synergistic role in cancer progression.
Area of Science:
- Oncology
- Molecular Biology
- Biochemistry
Background:
- Hyperactivated RAS signaling is a key driver in numerous human cancers.
- Oncogenic RAS activity is contingent upon the concurrent inactivation of protein phosphatase 2A (PP2A).
- The precise functional interactions between RAS and PP2A in regulating cellular pathways remain incompletely understood.
Purpose of the Study:
- To systematically investigate the functional interplay between RAS and PP2A in cancer.
- To identify and characterize phosphoproteome alterations regulated by RAS and PP2A.
- To develop and validate a novel normalization strategy for phosphoproteomic data.
Main Methods:
- Phosphoproteome analysis using mass spectrometry-based label-free quantification.
- Development and application of a novel pairwise normalization strategy for phosphopeptide abundance data.
- Bioinformatic analysis of phosphoproteomic datasets.
- Functional assays including colony growth assays and analysis of TCGA pan-cancer data.
Main Results:
- A novel pairwise normalization method was developed and validated for phosphoproteomic data analysis.
- Significant overlap was identified in the functional pathways regulated by RAS and CIP2A (a PP2A inhibitor).
- Synergistic effects on cell survival were observed between CIP2A expression and RAS activation, including KRAS mutations, in cancer datasets.
- Synergistic interactions between CIP2A and KRAS were confirmed in experimental depletion assays.
Conclusions:
- RAS and PP2A (inhibited by CIP2A) regulate overlapping signaling pathways crucial for cancer progression.
- The findings highlight a potential therapeutic vulnerability targeting the interplay between RAS and PP2A.
- The developed pairwise normalization method offers improved data analysis for phosphoproteomic studies with large directional changes.
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